Why MCP Is Preferred Over Skills

Recently, the AI industry has been pushing Skills as a new standard for giving LLMs capabilities, but its limitations in terms of actual service access are clear. Skills may be useful for simple knowledge transfer or teaching tool usage, but it is unsuitable as an architecture for connecting to actual services.

In contrast, MCP (Model Context Protocol) is built on the clear philosophy of API abstraction. The LLM doesn't need to understand the specific mechanics (How) of operation—it only needs to know what it can do (What). The actual operation is handled by the MCP server.

MCP offers the following powerful advantages:

• Remote usability and updates: Usable via a simple URL connection without local installation, and when the server is updated, changes are immediately reflected across all clients.
• Security and authentication: Handles authentication securely through OAuth and others, and through a sandbox environment, provides a controlled interface without granting the LLM excessive permissions.
• Portability and discovery: Works anywhere—Mac, mobile, web—and efficiently manages the Context Window by loading tools only when needed.

Skills, on the other hand, often requires CLI installation depending on the environment. This makes it impossible to run in web-based environments like ChatGPT or Perplexity, and also creates complex issues around API token management and deployment.